Comprehensive oculomotor behavioral response assessment (COBRA)

Patent metadata

Field	Value
Publication number	US-9730582-B1
Application number	US-201514710260-A
Country	US
Kind code	B1
Filing date	May 12, 2015
Priority date	May 16, 2014
Publication date	Aug 15, 2017
Grant date	Aug 15, 2017

Abstract

An eye movement-based methodology and assessment tool may be used to quantify many aspects of human dynamic visual processing using a relatively simple and short oculomotor task, noninvasive video-based eye tracking, and validated oculometric analysis techniques. By examining the eye movement responses to a task including a radially-organized appropriately randomized sequence of Rashbass-like step-ramp pursuit-tracking trials, distinct performance measurements may be generated that may be associated with, for example, pursuit initiation (e.g., latency and open-loop pursuit acceleration), steady-state tracking (e.g., gain, catch-up saccade amplitude, and the proportion of the steady-state response consisting of smooth movement), direction tuning (e.g., oblique effect amplitude, horizontal-vertical asymmetry, and direction noise), and speed tuning (e.g., speed responsiveness and noise). This quantitative approach may provide fast and results (e.g., a multi-dimensional set of oculometrics and a single scalar impairment index) that can be interpreted by one without a high degree of scientific sophistication or extensive training.

First claim

The invention claimed is:

1. A computer-implemented method, comprising:
displaying a tracking target, by a computing system, at an initial location on a display for a randomized delay interval;
after the randomized delay interval has elapsed,
moving the tracking target in a step, by the computing system, to a random location on the display,
moving the tracking target on the display, by the computing system, from the random location on the display towards the initial location at least until the tracking target crosses the initial location,
periodically measuring, by the computing system, user eye position while the user is following the tracking target, and
repeating the moving of the tracking target and eye position measurement, by the computing system, a plurality of times;

analyzing the user eye response measurements, by the computing system, to determine a plurality of quantitative performance measures; and
outputting, by the computing system, results of the analysis,
wherein the plurality of quantitative performance measurements comprise a cloverleaf as a measure of the user's own idiosyncratic oblique effect that provides a pattern uniquely identifying the user.

2. The computer-implemented method of claim 1 , wherein the cloverleaf as a measure of the user's own idiosyncratic oblique effect provides a baseline for the same user to determine deviation from normal performance for the user.

3. The computer-implemented method of claim 1 , wherein the cloverleaf as a measure of the user's own idiosyncratic oblique effect provides a measurement of user performance against a reference population of performance metrics from normal human subjects to determine a deviation from normal for the user.

4. The computer-implemented method of claim 1 , wherein the cloverleaf as a measure of the user's own idiosyncratic oblique effect provides a measurement of peripheral vision, prediction, asymmetry between eye performance, or any combination thereof, to determine a type and a degree of brain injury, a progression of disease, whether the user is faking an injury, whether the user is consciously failing to perform the task, or whether the user is intoxicated.

5. The computer-implemented method of claim 1 , wherein the cloverleaf as a measure of the user's own idiosyncratic oblique effect provides a baseline measurement from the same user for an injury to determine whether the user is faking the injury.

6. The computer-implemented method of claim 1 , further comprising:
comparing, by the computing system, a previous cloverleaf for the user to a current cloverleaf to determine whether the user is improving, deteriorating, or remaining the same.

7. A computer-implemented method, comprising:
displaying a tracking target, by a computing system, at an initial location on a display for a randomized delay interval;
after the randomized delay interval has elapsed,
moving the tracking target in a step, by the computing system, to a random location on the display,
moving the tracking target on the display, by the computing system, from the random location on the display towards the initial location at least until the tracking target crosses the initial location,
periodically measuring, by the computing system, user eye position while the user is following the tracking target, and
repeating the moving of the tracking target and eye position measurement, by the computing system, a plurality of times;

analyzing the user eye response measurements, by the computing system, to determine a plurality of quantitative performance measures; and
outputting, by the computing system, results of the analysis,
wherein the plurality of quantitative performance metrics comprise at least one metric for quantifying vigor of pursuit initiation and at least one metric for quantifying a quality of steady-state tracking.

8. The computer-implemented method of claim 7 , wherein the at least one metric for quantifying the vigor of the pursuit initiation quantifies latency and acceleration, and the at least one metric for quantifying the quality of the steady-state tracking quantifies gain, saccade amplitude, and proportion smooth.

9. A computer-implemented method, comprising:
displaying a tracking target, by a computing system, at an initial location on a display for a randomized delay interval;
after the randomized delay interval has elapsed,
moving the tracking target in a step, by the computing system, to a random location on the display,
moving the tracking target on the display, by the computing system, from the random location on the display towards the initial location at least until the tracking target crosses the initial location,
periodically measuring, by the computing system, user eye position while the user is following the tracking target, and
repeating the moving of the tracking target and eye position measurement, by the computing system, a plurality of times;

analyzing the user eye response measurements, by the computing system, to determine a plurality of quantitative performance measures; and
outputting, by the computing system, results of the analysis,
wherein the plurality of quantitative performance metrics comprise a direction of pursuit response, and a fitting function to describe a shape of a cloverleaf is determined by
f (φ)=1+α·cos(4(φ+Δ))−β·cos(2(φ+Δ))

where α describes a magnitude of cardinal-oblique anisotropy, β describes asymmetry between a size of vertical and horizontal lobes, and Δ describes an orientation of the cloverleaf.

10. The computer-implemented method of claim 7 , wherein the metrics for quantifying vigor of pursuit initiation and quantifying the quality of steady-state tracking provide a baseline for the same user to determine deviation from normal performance for the user.

11. The computer-implemented method of claim 7 , wherein the metrics for quantifying vigor of pursuit initiation and quantifying the quality of steady-state tracking provide a measurement of user performance against a reference population of performance metrics from normal human subjects to determine a deviation from normal for the user.

12. The computer-implemented method of claim 7 , wherein the metrics for quantifying vigor of pursuit initiation and quantifying the quality of steady-state tracking provide a measurement of peripheral vision, prediction, asymmetry between eye performance, or any combination thereof, to determine a type and a degree of brain injury, a progression of disease, whether the user is faking an injury, whether the user is consciously failing to perform the task, or whether the user is intoxicated.

13. The computer-implemented method of claim 7 , wherein the metrics for quantifying vigor of pursuit initiation and quantifying the quality of steady-state tracking provide a baseline measurement from the same user for an injury to determine whether the user is faking the injury.

14. The computer-implemented method of claim 7 , further comprising:
comparing, by the computing system, the metrics for quantifying vigor of pursuit initiation and quantifying the quality of steady-state tracking against previous measurements for the user to determine whether the user is improving, deteriorating, or remaining the same.

15. The computer-implemented method of claim 9 , wherein the direction of pursuit response provides a baseline for the same user to determine deviation from normal performance for the user.

16. The computer-implemented method of claim 9 , wherein the direction of pursuit response provides a measurement of user performance against a reference population of perfor

Assignees

• The Us Administrator Of The Nat Aeronautics & Space Administration (Nasa)
• Nasa

Inventors

• Stone Leland S
• Liston Dorion B

Classifications

• A61B3/0041

  characterised by display arrangements · CPC title

• A61B3/032

  Devices for presenting test symbols or characters, e.g. test chart projectors (A61B3/036 takes precedence) · CPC title

• A61B3/113 Primary

  for determining or recording eye movement · CPC title

• A61B3/0025

  characterised by electronic signal processing, e.g. eye models · CPC title

• A61B3/024

  for determining the visual field, e.g. perimeter types · CPC title